R Notebook
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Tips and troubleshooting: https://groundhogR.com
groundhog says:
OUTDATED GROUNDHOG
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install.packages('groundhog')pkgs <- c("tidyverse","here", "lmerTest", "sjPlot","broom.mixed", "kableExtra", "ggeffects", "gt", "brms", "bayestestR","ggdist", "pheatmap", "heatmaply","pheatmap","gplots","RColorBrewer")
groundhog.day <- '2022-07-25'
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── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──✔ ggplot2 3.3.6 ✔ purrr 0.3.4
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here() starts at /Users/jacobelder/Documents/GitHub/EpMemNet
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======================
Welcome to heatmaply version 1.3.0
Type citation('heatmaply') for how to cite the package.
Type ?heatmaply for the main documentation.
The github page is: https://github.com/talgalili/heatmaply/
Please submit your suggestions and bug-reports at: https://github.com/talgalili/heatmaply/issues
You may ask questions at stackoverflow, use the r and heatmaply tags:
https://stackoverflow.com/questions/tagged/heatmaply
======================
Succesfully attached 'heatmaply_1.3.0'
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Succesfully attached 'RColorBrewer_1.1-3'here::i_am("Analysis/idmPrelimAnal.Rmd")here() starts at /Users/jacobelder/Documents/GitHub/EpMemNetdevtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")ℹ SHA-1 hash of file is "07e3c11d2838efe15b1a6baf5ba2694da3f28cb1"devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")ℹ SHA-1 hash of file is "374a4de7fec345d21628a52c0ed0e4f2c389df8e"devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/named.effects.ref.R")ℹ SHA-1 hash of file is "0a5b928a75d310573e96ab72631b77a8a8b9acb3"fullLong <- arrow::read_parquet(here("Data", "longEpMNet.parquet"))
fullShort <- arrow::read_parquet(here("Data","shortEpMNet.parquet"))
fullLong$subID <- as.numeric(fullLong$subID)
fullData <- fullLong %>% full_join(fullShort, by = c("subID"))Wordcloud
#Create a vector containing only the text
text <- as.vector(fullData$memory)
# Create a corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documentsdocs <- tm_map(docs, content_transformer(tolower))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, stopwords("english"))Warning: transformation drops documentsdocs <- tm_map(docs, removeWords, c("the","and"))Warning: transformation drops documentsdtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df <- data.frame(word = names(words),freq=words)
wordcloud(words = df$word, freq = df$freq, min.freq = 1, max.words=200, random.order=FALSE, rot.per=0.35, colors=brewer.pal(8, "Dark2"))
H1: People will evaluate more positively, less negatively (i.e., more favorably) on memories with more downstram dependents.
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Val_2 ~ outdegree + indegree + (outdegree + indegree | subID)
Data: fullData
REML criterion at convergence: 15341.5
Scaled residuals:
Min 1Q Median 3Q Max
-1.8750 -0.7838 -0.1704 0.7634 2.5234
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 300.7515 17.3422
outdegree 0.7721 0.8787 -0.13
indegree 6.2828 2.5065 -0.65 0.83
Residual 1036.5181 32.1950
Number of obs: 1548, groups: subID, 207
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 42.3059 1.9074 125.5175 22.180 <2e-16 ***
outdegree 0.3745 0.4632 126.7401 0.808 0.420
indegree -0.8963 0.5666 34.2498 -1.582 0.123
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) outdgr
outdegree -0.390
indegree -0.513 0.017
optimizer (nloptwrap) convergence code: 0 (OK)
unable to evaluate scaled gradient
Model failed to converge: degenerate Hessian with 1 negative eigenvaluesH2: People will be more certain in memories with more downstream dependents.
m<-lmer(Cert ~ outdegree * indegree + ( outdegree + indegree | subID), data=fullData)
Warning: Model failed to converge with max|grad| = 0.00237505 (tol = 0.002, component 1)H3: Memories with more dependents will be more clearly defined and accessible.
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Clear ~ outdegree * indegree + (outdegree + indegree | subID)
Data: fullData
REML criterion at convergence: 6299.3
Scaled residuals:
Min 1Q Median 3Q Max
-5.1269 -0.4734 0.1503 0.6291 3.2023
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.549521 0.74130
outdegree 0.013024 0.11412 -0.44
indegree 0.001925 0.04387 -0.02 -0.45
Residual 1.022438 1.01116
Number of obs: 2065, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.78616 0.06838 231.33961 84.616 <2e-16 ***
outdegree 0.03795 0.02018 138.44596 1.880 0.0622 .
indegree 0.03698 0.01917 93.96452 1.929 0.0567 .
outdegree:indegree -0.00338 0.00403 32.54608 -0.839 0.4077
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) outdgr indegr
outdegree -0.491
indegree -0.381 0.154
outdgr:ndgr 0.300 -0.441 -0.666
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00215486 (tol = 0.002, component 1)H5: Memories with more dependents will be more fundamental to how people see themselves, and if they were changed, would change the person.
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ outdegree * indegree + (outdegree + indegree | subID)
Data: fullData
REML criterion at convergence: 7715.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.2162 -0.5220 0.1034 0.6270 2.5481
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 1.06273 1.0309
outdegree 0.01338 0.1157 -0.75
indegree 0.00126 0.0355 -0.01 0.31
Residual 2.07307 1.4398
Number of obs: 2067, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.789805 0.095453 240.070368 50.180 < 2e-16 ***
outdegree 0.190555 0.024236 82.278464 7.862 1.28e-11 ***
indegree 0.056842 0.025617 80.036007 2.219 0.0293 *
outdegree:indegree -0.008254 0.005521 25.194016 -1.495 0.1473
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) outdgr indegr
outdegree -0.583
indegree -0.400 0.332
outdgr:ndgr 0.302 -0.498 -0.705H6: Memories with more dependents will be more important to the person.
To Self
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ outdegree * indegree + (outdegree + indegree | subID)
Data: fullData
REML criterion at convergence: 6672.9
Scaled residuals:
Min 1Q Median 3Q Max
-4.5370 -0.4050 0.1854 0.6154 2.4371
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.516692 0.71881
outdegree 0.011485 0.10717 -0.57
indegree 0.000857 0.02927 -0.39 0.39
Residual 1.260789 1.12285
Number of obs: 2068, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.747140 0.070259 256.346056 81.799 < 2e-16 ***
outdegree 0.085016 0.020503 109.905889 4.147 6.67e-05 ***
indegree 0.052310 0.019705 70.486561 2.655 0.00981 **
outdegree:indegree -0.006379 0.004403 29.066621 -1.449 0.15816
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) outdgr indegr
outdegree -0.560
indegree -0.448 0.298
outdgr:ndgr 0.317 -0.455 -0.703
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00201471 (tol = 0.002, component 1)To Others
H9: People’s self-report of retrospected emotions during an experience will be associated with how positively or negatively they perceive the experience.
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Often ~ outdegree * indegree + (outdegree + indegree | subID)
Data: fullData
REML criterion at convergence: 7625.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.5030 -0.6726 -0.0755 0.6849 2.7581
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 1.196272 1.09374
outdegree 0.029577 0.17198 -0.34
indegree 0.001086 0.03296 -0.86 -0.20
Residual 2.011366 1.41823
Number of obs: 2043, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.388e+00 9.872e-02 2.487e+02 44.454 <2e-16 ***
outdegree 7.477e-02 2.908e-02 1.060e+02 2.571 0.0115 *
indegree 4.829e-02 2.426e-02 9.785e+02 1.990 0.0468 *
outdegree:indegree -5.575e-04 5.133e-03 1.384e+02 -0.109 0.9137
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) outdgr indegr
outdegree -0.467
indegree -0.432 0.218
outdgr:ndgr 0.307 -0.397 -0.766
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')H11: People think more often about memories with more memories causing them.
The more memories that depend on a given memory, the more people believe “This memory changed me”
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ outdegree * indegree + (outdegree + indegree | subID)
Data: fullData
REML criterion at convergence: 7357.6
Scaled residuals:
Min 1Q Median 3Q Max
-3.6619 -0.5006 0.1219 0.6297 2.7562
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.9236523 0.96107
outdegree 0.0081336 0.09019 -0.79
indegree 0.0005095 0.02257 -0.20 0.76
Residual 1.7462980 1.32148
Number of obs: 2067, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.102015 0.088091 250.970752 57.918 < 2e-16 ***
outdegree 0.144625 0.021061 172.110844 6.867 1.14e-10 ***
indegree 0.032704 0.022870 321.632075 1.430 0.154
outdegree:indegree -0.004158 0.004877 42.964756 -0.852 0.399
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) outdgr indegr
outdegree -0.581
indegree -0.408 0.381
outdgr:ndgr 0.304 -0.516 -0.732
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')The more memories that depend on a given memory, the more certain that people feel this experience is representative of who they are.
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Rep ~ outdegree * indegree + (outdegree + indegree | subID)
Data: fullData
REML criterion at convergence: 7571.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.3850 -0.5395 0.0710 0.6483 3.0274
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 1.105868 1.05160
outdegree 0.011932 0.10923 -0.49
indegree 0.006731 0.08204 -0.30 -0.02
Residual 1.892707 1.37576
Number of obs: 2067, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.738014 0.095535 258.205125 49.595 < 2e-16 ***
outdegree 0.124056 0.025023 129.805289 4.958 2.19e-06 ***
indegree 0.082239 0.027056 126.365172 3.040 0.00288 **
outdegree:indegree -0.008269 0.005843 143.902843 -1.415 0.15915
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) outdgr indegr
outdegree -0.479
indegree -0.434 0.246
outdgr:ndgr 0.284 -0.525 -0.609
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00496817 (tol = 0.002, component 1)Exploratory Analyses
Sentiment of memory will be associated with dependencies
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: vad_neg.x ~ outdegree + indegree + (outdegree + indegree | subID)
Data: fullData
REML criterion at convergence: -2395.5
Scaled residuals:
Min 1Q Median 3Q Max
-1.2736 -0.4135 -0.3078 -0.1937 6.5171
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 1.464e-03 0.038259
outdegree 2.728e-05 0.005223 -0.02
indegree 4.000e-06 0.002000 -1.00 0.00
Residual 1.924e-02 0.138703
Number of obs: 2281, groups: subID, 217
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.678e-02 5.343e-03 1.304e+02 10.628 <2e-16 ***
outdegree -7.797e-04 1.728e-03 8.273e+01 -0.451 0.653
indegree -8.582e-06 1.406e-03 2.234e+02 -0.006 0.995
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) outdgr
outdegree -0.405
indegree -0.404 -0.198
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: vad_pos.x ~ indegree * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: -1317.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.3643 -0.5609 -0.4600 0.2573 4.9936
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.00110 0.03317
Residual 0.03142 0.17727
Number of obs: 2281, groups: subID, 217
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 8.725e-02 7.031e-03 6.158e+02 12.409 < 2e-16 ***
indegree 9.378e-03 2.688e-03 2.126e+03 3.488 0.000496 ***
outdegree 4.411e-04 2.347e-03 1.959e+03 0.188 0.850934
indegree:outdegree -1.158e-03 5.544e-04 2.219e+03 -2.089 0.036804 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) indegr outdgr
indegree -0.581
outdegree -0.586 0.299
indegr:tdgr 0.479 -0.749 -0.566m<-lmer(IM ~ outdegree + indegree + ( outdegree + indegree | subID), data=fullData)Warning: Model failed to converge with max|grad| = 0.0153971 (tol = 0.002, component 1)summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ outdegree + indegree + (outdegree + indegree | subID)
Data: fullData
REML criterion at convergence: 6665.8
Scaled residuals:
Min 1Q Median 3Q Max
-4.5445 -0.4073 0.1879 0.6203 2.4769
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.5169032 0.71896
outdegree 0.0114980 0.10723 -0.56
indegree 0.0003298 0.01816 -0.68 0.57
Residual 1.2638297 1.12420
Number of obs: 2068, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.77913 0.06630 173.76150 87.160 < 2e-16 ***
outdegree 0.07156 0.01827 82.83632 3.917 0.000183 ***
indegree 0.03221 0.01290 2.05403 2.496 0.126674
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) outdgr
outdegree -0.491
indegree -0.328 -0.046
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.0153971 (tol = 0.002, component 1)m<-lmer(IM ~ strength + ( strength | subID), data=fullData)Warning: Model failed to converge with max|grad| = 92.0221 (tol = 0.002, component 1)Warning: Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ strength + (strength | subID)
Data: fullData
REML criterion at convergence: 6687.7
Scaled residuals:
Min 1Q Median 3Q Max
-4.5559 -0.4118 0.1778 0.5887 2.5612
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 1.199e+00 1.095211
strength 1.916e-06 0.001384 -0.83
Residual 1.201e+00 1.096010
Number of obs: 2068, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.794e+00 8.705e-02 7.182e+01 66.563 < 2e-16 ***
strength 8.144e-04 1.814e-04 3.410e+01 4.489 7.77e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
strength -0.670
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 92.0221 (tol = 0.002, component 1)
Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?m<-lmer(IO ~ outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)
m<-lmer(IO ~ strength + ( strength | subID), data=fullData)
summary(m)m<-glmer(outdegree ~ Val_1*Val_2 + ( Val_1+Val_2 | subID), data=fullData, family="poisson")
summary(m)
ggpredict(m, terms = c("Val_1","Val_2")) %>% plot()summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Val_1 ~ outdegree * indegree + (outdegree + indegree | subID)
Data: fullData
REML criterion at convergence: 18149.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.0913 -0.4947 0.2810 0.6610 2.0754
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 251.939 15.873
outdegree 3.624 1.904 -0.44
indegree 2.134 1.461 -0.61 0.02
Residual 810.698 28.473
Number of obs: 1879, groups: subID, 210
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 70.24459 1.70457 196.80392 41.210 <2e-16 ***
outdegree -0.07589 0.51228 101.96304 -0.148 0.8825
indegree 1.22299 0.53295 83.93875 2.295 0.0242 *
outdegree:indegree -0.05458 0.11869 37.70184 -0.460 0.6483
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) outdgr indegr
outdegree -0.513
indegree -0.534 0.243
outdgr:ndgr 0.354 -0.545 -0.652
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00493827 (tol = 0.002, component 1)m<-lmer(Val_2 ~ outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)
m<-lmer(Val_2 ~ strength + ( strength | subID), data=fullData)
summary(m)m<-lmer(Clear ~ outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)
m<-lmer(Clear ~ strength + ( strength | subID), data=fullData)
summary(m)m<-lmer(Breadth ~ outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)
m<-lmer(Breadth ~ strength + ( strength | subID), data=fullData)
summary(m)m<-lmer(Dist ~ outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)
m<-lmer(Dist ~ strength + ( strength | subID), data=fullData)
summary(m)m<-glmer(outdegree ~ scale(SO_1) * scale(SO_2) + ( 1 | subID), data=fullData,family="poisson")
summary(m)
ggpredict(m, terms = c("SO_1","SO_2")) %>% plot()m<-lmer( Fund ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SE * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7683.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.2098 -0.5290 0.1137 0.6371 2.4864
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7864 0.8868
Residual 2.1379 1.4622
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.275e+00 3.501e-01 2.761e+02 12.212 <2e-16 ***
SE 2.581e-01 1.528e-01 2.804e+02 1.689 0.0923 .
outdegree 1.361e-01 6.979e-02 2.031e+03 1.950 0.0513 .
SE:outdegree 2.583e-02 3.257e-02 2.030e+03 0.793 0.4279
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE outdgr
SE -0.974
outdegree -0.333 0.335
SE:outdegre 0.313 -0.335 -0.967m<-lmer( Fund ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SAM * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7549.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.2175 -0.5178 0.1063 0.6419 2.5699
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7361 0.858
Residual 2.1389 1.462
Number of obs: 2021, groups: subID, 205
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 3.65517 0.34689 295.35502 10.537 < 2e-16 ***
SAM 0.38011 0.10665 285.84081 3.564 0.000428 ***
outdegree 0.24059 0.09253 2016.77967 2.600 0.009385 **
SAM:outdegree -0.01742 0.02775 2015.59102 -0.628 0.530306
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SAM outdgr
SAM -0.974
outdegree -0.360 0.347
SAM:outdegr 0.356 -0.358 -0.981m<-lmer( Fund ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ CESD * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7684.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.1745 -0.5336 0.1117 0.6490 2.6458
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7868 0.887
Residual 2.1391 1.463
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.22605 0.34348 277.88771 12.304 < 2e-16 ***
CESD 0.28387 0.15012 272.11324 1.891 0.059696 .
outdegree 0.26814 0.08006 2030.09868 3.349 0.000825 ***
CESD:outdegree -0.03549 0.03441 2025.53893 -1.031 0.302512
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) CESD outdgr
CESD -0.973
outdegree -0.346 0.334
CESD:outdgr 0.339 -0.345 -0.975m<-lmer( Fund ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SOS * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7684.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.2164 -0.5324 0.1076 0.6382 2.5541
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7801 0.8832
Residual 2.1389 1.4625
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.151e+00 3.397e-01 2.693e+02 12.219 < 2e-16 ***
SOS 2.418e-01 1.133e-01 2.680e+02 2.134 0.03372 *
outdegree 1.879e-01 6.649e-02 2.012e+03 2.826 0.00476 **
SOS:outdegree 5.252e-04 2.321e-02 2.013e+03 0.023 0.98195
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SOS outdgr
SOS -0.973
outdegree -0.329 0.326
SOS:outdegr 0.310 -0.329 -0.964m<-lmer( Fund ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ DS * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7684.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.1906 -0.5193 0.0969 0.6308 2.4225
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7976 0.8931
Residual 2.1357 1.4614
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.12833 0.49225 278.82633 10.418 <2e-16 ***
DS -0.06920 0.12263 274.79489 -0.564 0.5730
outdegree -0.04272 0.10639 2039.19106 -0.402 0.6881
DS:outdegree 0.05965 0.02711 2035.46763 2.200 0.0279 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DS outdgr
DS -0.987
outdegree -0.335 0.334
DS:outdegre 0.326 -0.335 -0.986m<-lmer( Fund ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ MAIA * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7687.8
Scaled residuals:
Min 1Q Median 3Q Max
-3.1646 -0.5155 0.1022 0.6426 2.3918
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7984 0.8935
Residual 2.1391 1.4626
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.48539 0.47849 250.19012 9.374 < 2e-16 ***
MAIA 0.09801 0.12482 248.35032 0.785 0.433076
outdegree 0.29410 0.08819 2035.27152 3.335 0.000869 ***
MAIA:outdegree -0.02706 0.02199 2036.91686 -1.231 0.218639
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) MAIA outdgr
MAIA -0.986
outdegree -0.328 0.314
MAIA:outdgr 0.329 -0.327 -0.980m<-lmer( Fund ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ DT_P * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7674.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.1650 -0.5145 0.1130 0.6400 2.7859
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.8107 0.9004
Residual 2.1224 1.4569
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.55068 0.31153 276.69546 14.607 < 2e-16 ***
DT_P 0.13186 0.12454 283.89718 1.059 0.290599
outdegree 0.45316 0.07210 2035.70085 6.285 4e-10 ***
DT_P:outdegree -0.11384 0.03001 2043.27314 -3.793 0.000153 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DT_P outdgr
DT_P -0.967
outdegree -0.341 0.339
DT_P:outdgr 0.325 -0.344 -0.969m<-lmer( Fund ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ DT_M * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7686.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.1699 -0.5168 0.1039 0.6356 2.6418
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7911 0.8894
Residual 2.1387 1.4624
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.22306 0.36808 238.74422 11.473 < 2e-16 ***
DT_M 0.20404 0.11487 243.51099 1.776 0.076945 .
outdegree 0.25252 0.06569 2011.41871 3.844 0.000125 ***
DT_M:outdegree -0.02176 0.02146 2027.70946 -1.014 0.310826
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DT_M outdgr
DT_M -0.977
outdegree -0.306 0.306
DT_M:outdgr 0.293 -0.314 -0.963m<-lmer( Fund ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ NFC * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7689.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.1673 -0.5207 0.1023 0.6387 2.5883
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.8035 0.8964
Residual 2.1396 1.4627
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.90463 0.41350 267.01092 11.861 <2e-16 ***
NFC -0.01102 0.10471 264.95407 -0.105 0.916
outdegree 0.13822 0.08849 2018.72965 1.562 0.118
NFC:outdegree 0.01219 0.02123 2017.83732 0.574 0.566
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) NFC outdgr
NFC -0.981
outdegree -0.322 0.305
NFC:outdegr 0.323 -0.319 -0.980m<-lmer( Fund ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SCC * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7682
Scaled residuals:
Min 1Q Median 3Q Max
-3.1759 -0.5091 0.1079 0.6446 2.5120
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7679 0.8763
Residual 2.1385 1.4624
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.58979 0.31225 267.98502 17.902 < 2e-16 ***
SCC -0.25145 0.10347 271.53806 -2.430 0.01574 *
outdegree 0.22293 0.06974 2024.18454 3.197 0.00141 **
SCC:outdegree -0.01136 0.02241 2019.77420 -0.507 0.61230
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SCC outdgr
SCC -0.968
outdegree -0.336 0.318
SCC:outdegr 0.328 -0.333 -0.967fullData$PminN <- (fullData$Val_1-fullData$Val_2)
m<-lmer( PminN ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ SE * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 15421.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.5881 -0.5455 0.2077 0.7854 1.9727
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 382.2 19.55
Residual 3372.3 58.07
Number of obs: 1399, groups: subID, 202
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 60.469 12.079 337.154 5.006 8.97e-07 ***
SE -13.544 5.269 350.723 -2.570 0.0106 *
outdegree 4.393 3.285 1336.362 1.337 0.1813
SE:outdegree -2.352 1.526 1337.904 -1.541 0.1235
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE outdgr
SE -0.975
outdegree -0.519 0.520
SE:outdegre 0.487 -0.517 -0.970m<-lmer( PminN ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ SAM * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 15138.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.4973 -0.5590 0.2111 0.7756 1.9338
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 427.1 20.67
Residual 3416.4 58.45
Number of obs: 1371, groups: subID, 199
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 39.075 12.710 343.570 3.074 0.00228 **
SAM -3.170 3.849 324.713 -0.824 0.41074
outdegree 8.295 4.203 1217.855 1.973 0.04867 *
SAM:outdegree -2.557 1.239 1175.036 -2.064 0.03926 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SAM outdgr
SAM -0.976
outdegree -0.524 0.500
SAM:outdegr 0.515 -0.514 -0.982m<-lmer( PminN ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ CESD * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 15416.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.5919 -0.5456 0.1928 0.7810 1.9627
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 377.3 19.43
Residual 3362.7 57.99
Number of obs: 1399, groups: subID, 202
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 47.371 11.856 331.376 3.996 7.95e-05 ***
CESD -8.022 5.160 329.247 -1.554 0.12103
outdegree 10.696 3.709 1362.306 2.884 0.00399 **
CESD:outdegree -4.841 1.611 1356.346 -3.006 0.00270 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) CESD outdgr
CESD -0.974
outdegree -0.524 0.512
CESD:outdgr 0.513 -0.527 -0.977m<-lmer( PminN ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ SOS * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 15423.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.5548 -0.5384 0.1973 0.7751 1.9573
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 393.3 19.83
Residual 3369.6 58.05
Number of obs: 1399, groups: subID, 202
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 50.892 11.857 339.136 4.292 2.31e-05 ***
SOS -7.240 3.957 344.618 -1.830 0.0681 .
outdegree 6.449 3.265 1389.701 1.975 0.0484 *
SOS:outdegree -2.448 1.129 1383.971 -2.168 0.0303 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SOS outdgr
SOS -0.974
outdegree -0.525 0.517
SOS:outdegr 0.498 -0.519 -0.970m<-lmer( PminN ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ DS * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 15436.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.5300 -0.5476 0.2096 0.7680 1.9172
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 423.9 20.59
Residual 3388.6 58.21
Number of obs: 1399, groups: subID, 202
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 53.2900 17.3987 341.9508 3.063 0.00237 **
DS -5.9663 4.3111 336.9454 -1.384 0.16729
outdegree 1.0452 5.0343 1263.5368 0.208 0.83556
DS:outdegree -0.3525 1.2831 1290.2909 -0.275 0.78357
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DS outdgr
DS -0.988
outdegree -0.527 0.526
DS:outdegre 0.511 -0.524 -0.987m<-lmer( PminN ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ MAIA * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 15424.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.6524 -0.5416 0.2132 0.7783 1.9695
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 367.2 19.16
Residual 3384.2 58.17
Number of obs: 1399, groups: subID, 202
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -15.5434 15.4593 281.7684 -1.005 0.31555
MAIA 11.9574 4.0030 271.2232 2.987 0.00307 **
outdegree -3.2176 3.9571 1334.6026 -0.813 0.41629
MAIA:outdegree 0.7158 0.9825 1319.9343 0.729 0.46642
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) MAIA outdgr
MAIA -0.985
outdegree -0.530 0.509
MAIA:outdgr 0.530 -0.528 -0.980m<-lmer( PminN ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ DT_P * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 15435.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.5128 -0.5631 0.2061 0.7639 1.8896
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 409.3 20.23
Residual 3392.6 58.25
Number of obs: 1399, groups: subID, 202
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 49.9051 10.9751 372.2452 4.547 7.37e-06 ***
DT_P -8.4208 4.3826 390.9773 -1.921 0.0554 .
outdegree -1.2834 3.3680 1304.6733 -0.381 0.7032
DT_P:outdegree 0.4197 1.3930 1254.0608 0.301 0.7632
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DT_P outdgr
DT_P -0.969
outdegree -0.541 0.535
DT_P:outdgr 0.517 -0.543 -0.972m<-lmer( PminN ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ DT_M * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 15438.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.5339 -0.5573 0.2062 0.7749 1.9375
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 438.4 20.94
Residual 3386.4 58.19
Number of obs: 1399, groups: subID, 202
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 14.1544 12.7997 304.7340 1.106 0.270
DT_M 4.8324 3.9790 308.7030 1.214 0.225
outdegree 1.1927 3.1749 1387.9516 0.376 0.707
DT_M:outdegree -0.4546 1.0161 1363.0202 -0.447 0.655
Correlation of Fixed Effects:
(Intr) DT_M outdgr
DT_M -0.977
outdegree -0.509 0.502
DT_M:outdgr 0.487 -0.508 -0.968m<-lmer( PminN ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ NFC * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 15440
Scaled residuals:
Min 1Q Median 3Q Max
-2.5255 -0.5526 0.2089 0.7777 1.9309
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 447.1 21.14
Residual 3384.9 58.18
Number of obs: 1399, groups: subID, 202
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 30.7547 14.3671 331.3007 2.141 0.033 *
NFC -0.3257 3.6284 323.6829 -0.090 0.929
outdegree -2.8860 3.9391 1393.7161 -0.733 0.464
NFC:outdegree 0.6554 0.9528 1394.1076 0.688 0.492
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) NFC outdgr
NFC -0.981
outdegree -0.498 0.476
NFC:outdegr 0.498 -0.497 -0.979m<-lmer( PminN ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ SCC * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 15430.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.5363 -0.5486 0.2086 0.7702 1.9420
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 414.7 20.36
Residual 3375.8 58.10
Number of obs: 1399, groups: subID, 202
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 17.156 10.955 333.903 1.566 0.1183
SCC 4.230 3.649 335.967 1.159 0.2471
outdegree -6.460 3.272 1372.178 -1.974 0.0485 *
SCC:outdegree 2.076 1.070 1385.385 1.940 0.0526 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SCC outdgr
SCC -0.969
outdegree -0.507 0.489
SCC:outdegr 0.498 -0.512 -0.970m<-lmer( Fund ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SE * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7683.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.2098 -0.5290 0.1137 0.6371 2.4864
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7864 0.8868
Residual 2.1379 1.4622
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.275e+00 3.501e-01 2.761e+02 12.212 <2e-16 ***
SE 2.581e-01 1.528e-01 2.804e+02 1.689 0.0923 .
outdegree 1.361e-01 6.979e-02 2.031e+03 1.950 0.0513 .
SE:outdegree 2.583e-02 3.257e-02 2.030e+03 0.793 0.4279
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE outdgr
SE -0.974
outdegree -0.333 0.335
SE:outdegre 0.313 -0.335 -0.967m<-lmer( Fund ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SAM * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7549.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.2175 -0.5178 0.1063 0.6419 2.5699
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7361 0.858
Residual 2.1389 1.462
Number of obs: 2021, groups: subID, 205
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 3.65517 0.34689 295.35502 10.537 < 2e-16 ***
SAM 0.38011 0.10665 285.84081 3.564 0.000428 ***
outdegree 0.24059 0.09253 2016.77967 2.600 0.009385 **
SAM:outdegree -0.01742 0.02775 2015.59102 -0.628 0.530306
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SAM outdgr
SAM -0.974
outdegree -0.360 0.347
SAM:outdegr 0.356 -0.358 -0.981m<-lmer( Fund ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ CESD * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7684.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.1745 -0.5336 0.1117 0.6490 2.6458
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7868 0.887
Residual 2.1391 1.463
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.22605 0.34348 277.88771 12.304 < 2e-16 ***
CESD 0.28387 0.15012 272.11324 1.891 0.059696 .
outdegree 0.26814 0.08006 2030.09868 3.349 0.000825 ***
CESD:outdegree -0.03549 0.03441 2025.53893 -1.031 0.302512
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) CESD outdgr
CESD -0.973
outdegree -0.346 0.334
CESD:outdgr 0.339 -0.345 -0.975m<-lmer( Fund ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SOS * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7684.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.2164 -0.5324 0.1076 0.6382 2.5541
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7801 0.8832
Residual 2.1389 1.4625
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.151e+00 3.397e-01 2.693e+02 12.219 < 2e-16 ***
SOS 2.418e-01 1.133e-01 2.680e+02 2.134 0.03372 *
outdegree 1.879e-01 6.649e-02 2.012e+03 2.826 0.00476 **
SOS:outdegree 5.252e-04 2.321e-02 2.013e+03 0.023 0.98195
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SOS outdgr
SOS -0.973
outdegree -0.329 0.326
SOS:outdegr 0.310 -0.329 -0.964m<-lmer( Fund ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ DS * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7684.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.1906 -0.5193 0.0969 0.6308 2.4225
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7976 0.8931
Residual 2.1357 1.4614
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.12833 0.49225 278.82633 10.418 <2e-16 ***
DS -0.06920 0.12263 274.79489 -0.564 0.5730
outdegree -0.04272 0.10639 2039.19106 -0.402 0.6881
DS:outdegree 0.05965 0.02711 2035.46763 2.200 0.0279 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DS outdgr
DS -0.987
outdegree -0.335 0.334
DS:outdegre 0.326 -0.335 -0.986m<-lmer( Fund ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ MAIA * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7687.8
Scaled residuals:
Min 1Q Median 3Q Max
-3.1646 -0.5155 0.1022 0.6426 2.3918
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7984 0.8935
Residual 2.1391 1.4626
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.48539 0.47849 250.19012 9.374 < 2e-16 ***
MAIA 0.09801 0.12482 248.35032 0.785 0.433076
outdegree 0.29410 0.08819 2035.27152 3.335 0.000869 ***
MAIA:outdegree -0.02706 0.02199 2036.91686 -1.231 0.218639
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) MAIA outdgr
MAIA -0.986
outdegree -0.328 0.314
MAIA:outdgr 0.329 -0.327 -0.980m<-lmer( Fund ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ DT_P * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7674.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.1650 -0.5145 0.1130 0.6400 2.7859
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.8107 0.9004
Residual 2.1224 1.4569
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.55068 0.31153 276.69546 14.607 < 2e-16 ***
DT_P 0.13186 0.12454 283.89718 1.059 0.290599
outdegree 0.45316 0.07210 2035.70085 6.285 4e-10 ***
DT_P:outdegree -0.11384 0.03001 2043.27314 -3.793 0.000153 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DT_P outdgr
DT_P -0.967
outdegree -0.341 0.339
DT_P:outdgr 0.325 -0.344 -0.969m<-lmer( Fund ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ DT_M * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7686.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.1699 -0.5168 0.1039 0.6356 2.6418
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7911 0.8894
Residual 2.1387 1.4624
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.22306 0.36808 238.74422 11.473 < 2e-16 ***
DT_M 0.20404 0.11487 243.51099 1.776 0.076945 .
outdegree 0.25252 0.06569 2011.41871 3.844 0.000125 ***
DT_M:outdegree -0.02176 0.02146 2027.70946 -1.014 0.310826
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DT_M outdgr
DT_M -0.977
outdegree -0.306 0.306
DT_M:outdgr 0.293 -0.314 -0.963m<-lmer( Fund ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ NFC * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7689.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.1673 -0.5207 0.1023 0.6387 2.5883
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.8035 0.8964
Residual 2.1396 1.4627
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.90463 0.41350 267.01092 11.861 <2e-16 ***
NFC -0.01102 0.10471 264.95407 -0.105 0.916
outdegree 0.13822 0.08849 2018.72965 1.562 0.118
NFC:outdegree 0.01219 0.02123 2017.83732 0.574 0.566
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) NFC outdgr
NFC -0.981
outdegree -0.322 0.305
NFC:outdegr 0.323 -0.319 -0.980m<-lmer( Fund ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SCC * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7682
Scaled residuals:
Min 1Q Median 3Q Max
-3.1759 -0.5091 0.1079 0.6446 2.5120
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7679 0.8763
Residual 2.1385 1.4624
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.58979 0.31225 267.98502 17.902 < 2e-16 ***
SCC -0.25145 0.10347 271.53806 -2.430 0.01574 *
outdegree 0.22293 0.06974 2024.18454 3.197 0.00141 **
SCC:outdegree -0.01136 0.02241 2019.77420 -0.507 0.61230
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SCC outdgr
SCC -0.968
outdegree -0.336 0.318
SCC:outdegr 0.328 -0.333 -0.967m<-lmer( Chan ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ SE * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7313.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.6328 -0.5180 0.1212 0.6408 2.4006
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.6904 0.8309
Residual 1.7782 1.3335
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.584e+00 3.247e-01 2.738e+02 14.117 <2e-16 ***
SE 2.382e-01 1.417e-01 2.778e+02 1.681 0.0939 .
outdegree 9.624e-02 6.373e-02 2.027e+03 1.510 0.1312
SE:outdegree 2.984e-02 2.974e-02 2.026e+03 1.003 0.3158
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE outdgr
SE -0.974
outdegree -0.327 0.329
SE:outdegre 0.307 -0.329 -0.967m<-lmer( Chan ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ SAM * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7183.9
Scaled residuals:
Min 1Q Median 3Q Max
-3.6473 -0.5208 0.1206 0.6371 2.4250
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.6394 0.7996
Residual 1.7793 1.3339
Number of obs: 2021, groups: subID, 205
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.156e+00 3.205e-01 2.914e+02 12.970 < 2e-16 ***
SAM 3.060e-01 9.855e-02 2.822e+02 3.105 0.00209 **
outdegree 9.430e-02 8.452e-02 2.017e+03 1.116 0.26467
SAM:outdegree 1.808e-02 2.535e-02 2.017e+03 0.713 0.47577
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SAM outdgr
SAM -0.974
outdegree -0.355 0.342
SAM:outdegr 0.351 -0.353 -0.981m<-lmer( Chan ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ CESD * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7314.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.6559 -0.5191 0.1332 0.6463 2.4014
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.6856 0.828
Residual 1.7801 1.334
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.46452 0.31787 276.49130 14.045 < 2e-16 ***
CESD 0.29616 0.13895 270.86164 2.131 0.03396 *
outdegree 0.19516 0.07311 2026.81987 2.669 0.00766 **
CESD:outdegree -0.01721 0.03143 2022.05907 -0.548 0.58408
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) CESD outdgr
CESD -0.973
outdegree -0.341 0.328
CESD:outdgr 0.334 -0.339 -0.975m<-lmer( Chan ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ SOS * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7314.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.6383 -0.5085 0.1240 0.6448 2.4067
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.6817 0.8257
Residual 1.7795 1.3340
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.480e+00 3.147e-01 2.671e+02 14.236 <2e-16 ***
SOS 2.192e-01 1.050e-01 2.659e+02 2.088 0.0377 *
outdegree 1.252e-01 6.070e-02 2.008e+03 2.062 0.0393 *
SOS:outdegree 1.182e-02 2.119e-02 2.009e+03 0.558 0.5771
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SOS outdgr
SOS -0.973
outdegree -0.323 0.320
SOS:outdegr 0.305 -0.323 -0.964m<-lmer( Chan ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ DS * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7313.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.6174 -0.5013 0.1230 0.6416 2.3948
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7004 0.8369
Residual 1.7757 1.3325
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.19054 0.45657 276.35989 11.369 <2e-16 ***
DS -0.01804 0.11376 272.49764 -0.159 0.8741
outdegree -0.07279 0.09715 2035.36866 -0.749 0.4538
DS:outdegree 0.05934 0.02475 2031.38317 2.397 0.0166 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DS outdgr
DS -0.987
outdegree -0.328 0.328
DS:outdegre 0.320 -0.329 -0.986m<-lmer( Chan ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ MAIA * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7318.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.5986 -0.5204 0.1230 0.6409 2.3870
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7004 0.8369
Residual 1.7798 1.3341
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.81662 0.44413 248.77477 10.845 <2e-16 ***
MAIA 0.08051 0.11586 246.98514 0.695 0.4878
outdegree 0.25949 0.08055 2031.60048 3.221 0.0013 **
MAIA:outdegree -0.02624 0.02009 2033.26793 -1.306 0.1917
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) MAIA outdgr
MAIA -0.986
outdegree -0.321 0.308
MAIA:outdgr 0.323 -0.321 -0.980m<-lmer( Chan ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ DT_P * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7309.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.6221 -0.5225 0.1448 0.6421 2.4822
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7054 0.8399
Residual 1.7709 1.3307
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.10155 0.28835 274.20611 17.692 < 2e-16 ***
DT_P 0.01278 0.11526 281.15601 0.111 0.91182
outdegree 0.35222 0.06593 2032.84483 5.342 1.02e-07 ***
DT_P:outdegree -0.08412 0.02745 2040.98547 -3.065 0.00221 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DT_P outdgr
DT_P -0.967
outdegree -0.336 0.334
DT_P:outdgr 0.320 -0.339 -0.969m<-lmer( Chan ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ DT_M * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7318.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.6061 -0.5128 0.1281 0.6473 2.4047
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.6905 0.831
Residual 1.7814 1.335
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.638e+00 3.413e-01 2.365e+02 13.590 < 2e-16 ***
DT_M 1.552e-01 1.065e-01 2.411e+02 1.457 0.14642
outdegree 1.821e-01 6.001e-02 2.007e+03 3.034 0.00245 **
DT_M:outdegree -8.553e-03 1.961e-02 2.024e+03 -0.436 0.66279
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DT_M outdgr
DT_M -0.977
outdegree -0.300 0.301
DT_M:outdgr 0.288 -0.309 -0.963m<-lmer( Chan ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ NFC * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7320.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.6132 -0.5153 0.1282 0.6462 2.4070
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.7018 0.8378
Residual 1.7808 1.3345
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.944e+00 3.831e-01 2.655e+02 12.905 <2e-16 ***
NFC 4.697e-02 9.702e-02 2.635e+02 0.484 0.6287
outdegree 1.394e-01 8.081e-02 2.015e+03 1.725 0.0846 .
NFC:outdegree 4.115e-03 1.939e-02 2.014e+03 0.212 0.8319
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) NFC outdgr
NFC -0.981
outdegree -0.317 0.300
NFC:outdegr 0.318 -0.314 -0.980m<-lmer( Chan ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ SCC * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 7310.6
Scaled residuals:
Min 1Q Median 3Q Max
-3.6373 -0.5116 0.1253 0.6378 2.4107
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.6713 0.8193
Residual 1.7781 1.3334
Number of obs: 2055, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.83642 0.28929 267.27206 20.175 < 2e-16 ***
SCC -0.24528 0.09585 270.73889 -2.559 0.011044 *
outdegree 0.21139 0.06366 2020.25543 3.321 0.000914 ***
SCC:outdegree -0.01797 0.02045 2015.82415 -0.879 0.379653
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SCC outdgr
SCC -0.968
outdegree -0.330 0.312
SCC:outdegr 0.323 -0.327 -0.967m<-lmer( Cert ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ SE * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6411.1
Scaled residuals:
Min 1Q Median 3Q Max
-4.5927 -0.5513 0.1285 0.5886 2.7481
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.5678 0.7535
Residual 1.1658 1.0797
Number of obs: 2032, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.75297 0.28324 259.29915 20.311 <2e-16 ***
SE -0.10839 0.12354 262.42119 -0.877 0.381
outdegree 0.07994 0.05186 1983.57725 1.541 0.123
SE:outdegree -0.01546 0.02421 1982.27728 -0.639 0.523
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE outdgr
SE -0.974
outdegree -0.301 0.303
SE:outdegre 0.282 -0.303 -0.967m<-lmer( Cert ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ SAM * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6283.1
Scaled residuals:
Min 1Q Median 3Q Max
-4.0666 -0.5461 0.1432 0.5925 2.7687
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.5636 0.7507
Residual 1.1526 1.0736
Number of obs: 1998, groups: subID, 205
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.04752 0.28422 271.64450 17.760 <2e-16 ***
SAM 0.14529 0.08753 264.36833 1.660 0.0981 .
outdegree 0.15488 0.06885 1983.29226 2.250 0.0246 *
SAM:outdegree -0.03216 0.02066 1986.07005 -1.557 0.1197
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SAM outdgr
SAM -0.974
outdegree -0.320 0.308
SAM:outdegr 0.316 -0.319 -0.982m<-lmer( Cert ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ CESD * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6412
Scaled residuals:
Min 1Q Median 3Q Max
-4.6236 -0.5465 0.1304 0.5875 2.7673
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.5747 0.7581
Residual 1.1652 1.0794
Number of obs: 2032, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.403e+00 2.789e-01 2.606e+02 19.371 <2e-16 ***
CESD 4.733e-02 1.221e-01 2.561e+02 0.388 0.698
outdegree 1.377e-02 5.950e-02 1.982e+03 0.231 0.817
CESD:outdegree 1.527e-02 2.558e-02 1.977e+03 0.597 0.551
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) CESD outdgr
CESD -0.973
outdegree -0.313 0.301
CESD:outdgr 0.306 -0.311 -0.975m<-lmer( Cert ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ SOS * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6414
Scaled residuals:
Min 1Q Median 3Q Max
-4.6101 -0.5436 0.1300 0.5814 2.7495
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.5749 0.7582
Residual 1.1656 1.0796
Number of obs: 2032, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.513e+00 2.771e-01 2.533e+02 19.896 <2e-16 ***
SOS -1.982e-03 9.244e-02 2.521e+02 -0.021 0.983
outdegree 4.139e-02 4.936e-02 1.962e+03 0.838 0.402
SOS:outdegree 2.580e-03 1.723e-02 1.962e+03 0.150 0.881
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SOS outdgr
SOS -0.973
outdegree -0.294 0.291
SOS:outdegr 0.277 -0.294 -0.963m<-lmer( Cert ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ DS * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6408.7
Scaled residuals:
Min 1Q Median 3Q Max
-4.6060 -0.5401 0.1304 0.5970 2.7560
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.5549 0.7449
Residual 1.1660 1.0798
Number of obs: 2032, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 6.323e+00 3.938e-01 2.657e+02 16.055 <2e-16 ***
DS -2.057e-01 9.816e-02 2.626e+02 -2.096 0.0371 *
outdegree 5.013e-02 7.922e-02 1.995e+03 0.633 0.5269
DS:outdegree -5.535e-04 2.019e-02 1.991e+03 -0.027 0.9781
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DS outdgr
DS -0.987
outdegree -0.308 0.307
DS:outdegre 0.300 -0.309 -0.986m<-lmer( Cert ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ MAIA * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6404.7
Scaled residuals:
Min 1Q Median 3Q Max
-4.5952 -0.5469 0.1288 0.6050 2.7598
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.5408 0.7354
Residual 1.1656 1.0796
Number of obs: 2032, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.368e+00 3.805e-01 2.443e+02 11.478 < 2e-16 ***
MAIA 3.016e-01 9.927e-02 2.424e+02 3.038 0.00264 **
outdegree 7.443e-02 6.573e-02 1.995e+03 1.132 0.25764
MAIA:outdegree -6.885e-03 1.638e-02 1.997e+03 -0.420 0.67428
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) MAIA outdgr
MAIA -0.986
outdegree -0.304 0.291
MAIA:outdgr 0.305 -0.303 -0.980m<-lmer( Cert ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ DT_P * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6408.7
Scaled residuals:
Min 1Q Median 3Q Max
-4.6144 -0.5376 0.1364 0.5934 2.7674
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.5591 0.7478
Residual 1.1654 1.0795
Number of obs: 2032, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 6.02338 0.24867 262.42292 24.223 <2e-16 ***
DT_P -0.21320 0.09934 268.39906 -2.146 0.0328 *
outdegree 0.02214 0.05376 1994.70771 0.412 0.6805
DT_P:outdegree 0.01111 0.02240 2004.76783 0.496 0.6199
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DT_P outdgr
DT_P -0.967
outdegree -0.315 0.313
DT_P:outdgr 0.300 -0.318 -0.969m<-lmer( Cert ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ DT_M * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6413.1
Scaled residuals:
Min 1Q Median 3Q Max
-4.6149 -0.5481 0.1351 0.5941 2.7617
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.5728 0.7568
Residual 1.1653 1.0795
Number of obs: 2032, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.40232 0.30003 231.45381 18.006 <2e-16 ***
DT_M 0.03481 0.09354 235.17116 0.372 0.7101
outdegree 0.09691 0.04878 1961.86630 1.987 0.0471 *
DT_M:outdegree -0.01644 0.01595 1979.03059 -1.031 0.3029
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DT_M outdgr
DT_M -0.977
outdegree -0.273 0.274
DT_M:outdgr 0.262 -0.281 -0.963m<-lmer( Cert ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ NFC * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6409.2
Scaled residuals:
Min 1Q Median 3Q Max
-4.6107 -0.5452 0.1406 0.5826 2.7588
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.5605 0.7486
Residual 1.1648 1.0793
Number of obs: 2032, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.775e+00 3.314e-01 2.587e+02 14.409 <2e-16 ***
NFC 1.891e-01 8.396e-02 2.568e+02 2.253 0.0251 *
outdegree 7.113e-02 6.580e-02 1.975e+03 1.081 0.2799
NFC:outdegree -5.794e-03 1.578e-02 1.974e+03 -0.367 0.7135
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) NFC outdgr
NFC -0.981
outdegree -0.296 0.279
NFC:outdegr 0.296 -0.292 -0.980m<-lmer( Cert ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ SCC * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6412.9
Scaled residuals:
Min 1Q Median 3Q Max
-4.6124 -0.5531 0.1417 0.5784 2.7739
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.5717 0.7561
Residual 1.1653 1.0795
Number of obs: 2032, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.653e+00 2.557e-01 2.524e+02 22.107 <2e-16 ***
SCC -4.943e-02 8.468e-02 2.551e+02 -0.584 0.560
outdegree -8.389e-03 5.192e-02 1.973e+03 -0.162 0.872
SCC:outdegree 1.890e-02 1.667e-02 1.968e+03 1.134 0.257
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SCC outdgr
SCC -0.968
outdegree -0.301 0.285
SCC:outdegr 0.294 -0.298 -0.967m<-lmer( IM ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ SE * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6655.8
Scaled residuals:
Min 1Q Median 3Q Max
-4.4750 -0.4369 0.1990 0.6372 2.3341
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3766 0.6137
Residual 1.3151 1.1468
Number of obs: 2056, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.90386 0.25482 309.18613 23.168 <2e-16 ***
SE -0.03643 0.11127 314.85893 -0.327 0.7436
outdegree 0.10445 0.05443 2047.27892 1.919 0.0551 .
SE:outdegree -0.01110 0.02538 2046.40030 -0.437 0.6619
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE outdgr
SE -0.974
outdegree -0.363 0.365
SE:outdegre 0.340 -0.365 -0.967m<-lmer( IM ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ SAM * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6535.4
Scaled residuals:
Min 1Q Median 3Q Max
-4.4798 -0.3985 0.2003 0.6491 2.3147
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3355 0.5793
Residual 1.3173 1.1477
Number of obs: 2022, groups: subID, 205
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.00158 0.25048 329.65913 19.968 < 2e-16 ***
SAM 0.26042 0.07690 317.61137 3.387 0.000797 ***
outdegree 0.19075 0.07188 2001.96114 2.654 0.008023 **
SAM:outdegree -0.03425 0.02156 1993.74243 -1.589 0.112277
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SAM outdgr
SAM -0.975
outdegree -0.395 0.381
SAM:outdegr 0.390 -0.394 -0.981m<-lmer( IM ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ CESD * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6649.6
Scaled residuals:
Min 1Q Median 3Q Max
-4.4529 -0.4378 0.1831 0.6461 2.3316
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.376 0.6132
Residual 1.311 1.1450
Number of obs: 2056, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.52096 0.24978 312.02387 22.103 < 2e-16 ***
CESD 0.13318 0.10906 304.74086 1.221 0.222960
outdegree 0.23630 0.06230 2046.38699 3.793 0.000153 ***
CESD:outdegree -0.06810 0.02677 2043.66047 -2.544 0.011040 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) CESD outdgr
CESD -0.973
outdegree -0.376 0.363
CESD:outdgr 0.368 -0.375 -0.975m<-lmer( IM ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ SOS * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6656.4
Scaled residuals:
Min 1Q Median 3Q Max
-4.4750 -0.4254 0.1928 0.6399 2.3352
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.377 0.614
Residual 1.315 1.147
Number of obs: 2056, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.67280 0.24750 297.45064 22.921 <2e-16 ***
SOS 0.05134 0.08255 296.32342 0.622 0.5345
outdegree 0.13255 0.05175 2032.23331 2.561 0.0105 *
SOS:outdegree -0.01834 0.01808 2032.65346 -1.015 0.3104
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SOS outdgr
SOS -0.973
outdegree -0.359 0.355
SOS:outdegr 0.338 -0.358 -0.964m<-lmer( IM ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ DS * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6656.8
Scaled residuals:
Min 1Q Median 3Q Max
-4.4928 -0.4372 0.1999 0.6372 2.3324
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3759 0.6131
Residual 1.3154 1.1469
Number of obs: 2056, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.969e+00 3.569e-01 3.150e+02 16.723 <2e-16 ***
DS -3.767e-02 8.885e-02 3.096e+02 -0.424 0.672
outdegree 4.352e-02 8.294e-02 2.052e+03 0.525 0.600
DS:outdegree 9.859e-03 2.113e-02 2.050e+03 0.467 0.641
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DS outdgr
DS -0.987
outdegree -0.366 0.366
DS:outdegre 0.357 -0.367 -0.986m<-lmer( IM ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ MAIA * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6649.4
Scaled residuals:
Min 1Q Median 3Q Max
-4.4874 -0.4236 0.1942 0.6388 2.3225
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3635 0.6029
Residual 1.3131 1.1459
Number of obs: 2056, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.127e+00 3.415e-01 2.830e+02 15.014 <2e-16 ***
MAIA 1.843e-01 8.905e-02 2.807e+02 2.070 0.0394 *
outdegree 7.618e-03 6.860e-02 2.051e+03 0.111 0.9116
MAIA:outdegree 1.852e-02 1.711e-02 2.051e+03 1.082 0.2793
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) MAIA outdgr
MAIA -0.986
outdegree -0.364 0.349
MAIA:outdgr 0.366 -0.364 -0.980m<-lmer( IM ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ DT_P * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6635.3
Scaled residuals:
Min 1Q Median 3Q Max
-4.4636 -0.4326 0.1969 0.6580 2.3606
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3682 0.6068
Residual 1.3028 1.1414
Number of obs: 2056, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.85663 0.22300 315.33315 26.262 < 2e-16 ***
DT_P -0.01160 0.08922 324.38151 -0.130 0.897
outdegree 0.31074 0.05573 2050.41119 5.575 2.80e-08 ***
DT_P:outdegree -0.09831 0.02313 2051.99969 -4.250 2.24e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DT_P outdgr
DT_P -0.967
outdegree -0.378 0.375
DT_P:outdgr 0.361 -0.381 -0.969m<-lmer( IM ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ DT_M * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6649.5
Scaled residuals:
Min 1Q Median 3Q Max
-4.4731 -0.4159 0.1939 0.6427 2.3471
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3648 0.604
Residual 1.3128 1.146
Number of obs: 2056, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.16018 0.26312 263.08052 19.612 < 2e-16 ***
DT_M 0.21244 0.08218 268.96472 2.585 0.010258 *
outdegree 0.18295 0.05115 2035.55193 3.577 0.000356 ***
DT_M:outdegree -0.03412 0.01668 2046.37759 -2.046 0.040911 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DT_M outdgr
DT_M -0.976
outdegree -0.342 0.341
DT_M:outdgr 0.327 -0.350 -0.963m<-lmer( IM ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ NFC * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6657.1
Scaled residuals:
Min 1Q Median 3Q Max
-4.4811 -0.4264 0.1991 0.6356 2.3354
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3787 0.6154
Residual 1.3144 1.1465
Number of obs: 2056, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.729e+00 2.990e-01 3.005e+02 19.159 <2e-16 ***
NFC 2.419e-02 7.569e-02 2.978e+02 0.320 0.750
outdegree 3.698e-02 6.894e-02 2.039e+03 0.536 0.592
NFC:outdegree 1.088e-02 1.654e-02 2.039e+03 0.658 0.511
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) NFC outdgr
NFC -0.981
outdegree -0.353 0.335
NFC:outdegr 0.354 -0.350 -0.980m<-lmer( IM ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ SCC * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 6652.8
Scaled residuals:
Min 1Q Median 3Q Max
-4.4753 -0.4283 0.2009 0.6488 2.3293
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 0.3692 0.6076
Residual 1.3138 1.1462
Number of obs: 2056, groups: subID, 208
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 6.273e+00 2.273e-01 2.956e+02 27.604 <2e-16 ***
SCC -1.549e-01 7.535e-02 3.000e+02 -2.055 0.0407 *
outdegree -1.952e-04 5.433e-02 2.043e+03 -0.004 0.9971
SCC:outdegree 2.737e-02 1.746e-02 2.040e+03 1.567 0.1172
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SCC outdgr
SCC -0.968
outdegree -0.365 0.346
SCC:outdegr 0.357 -0.362 -0.967m<-lmer( Clear ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)m<-lmer( Rep ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)Indegree Cross-Level Interaction
fullData$PminN <- (fullData$Val_1-fullData$Val_2)
m<-lmer( PminN ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( PminN ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( PminN ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( PminN ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( PminN ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( PminN ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( PminN ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( PminN ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( PminN ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( PminN ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)m<-lmer( Fund ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Fund ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Fund ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Fund ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Fund ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Fund ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Fund ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Fund ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Fund ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Fund ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)m<-lmer( Chan ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Chan ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Chan ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("CESD","indegree")) %>% plot()
m<-lmer( Chan ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("SOS","indegree")) %>% plot()
m<-lmer( Chan ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Chan ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Chan ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Chan ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Chan ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Chan ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("SCC","indegree")) %>% plot()m<-lmer( Cert ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Cert ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Cert ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Cert ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Cert ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Cert ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Cert ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Cert ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Cert ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Cert ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)m<-lmer( IM ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( IM ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( IM ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( IM ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( IM ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( IM ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( IM ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( IM ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( IM ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( IM ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)m<-lmer( Clear ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)m<-lmer( Rep ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)Do people higher in self-esteem have more self than other focused memories?
fullData$SminO <- fullData$SO_1 - fullData$SO_2
m<-lmer( SminO ~ SE + ( 1 | subID), data=fullData)
summary(m)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: SminO ~ SE + (1 | subID)
Data: fullData
REML criterion at convergence: 17813.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.8301 -0.5846 -0.1584 0.6071 2.8011
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 287 16.94
Residual 1191 34.50
Number of obs: 1776, groups: subID, 206
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 20.1316 6.8611 197.7275 2.934 0.00374 **
SE 0.4568 2.9956 200.2941 0.152 0.87896
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
SE -0.974fullShort <- do.call(data.frame, # Replace Inf in data by NA
lapply(fullShort,
function(x) replace(x, is.infinite(x), NA)))
corMat <- fullShort %>% select(edgeTot:NFC) %>% cor(fullShort,use="pairwise.complete.obs")
outphm <- pheatmap(corMat, fontsize_row = 6, fontsize_col = 6, angle_col = 45, angle_row =45, width=100, height = 200 )
heatmaply_cor(round(corMat,3), Rowv=outphm[[1]], Colv=outphm[[2]], revC=TRUE, fontsize_row = 2.5, fontsize_col = 2.5, angle_col = 45, angle_row =45, limits = c(-1, 1), colors = colorRampPalette(rev(brewer.pal(n = 7, name =
"RdYlBu")))(100) )fullShort %>% select(vad_comp, MAIA:NFC) %>% corToOne(., "vad_comp")Loading required package: corrr
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(referenceVar)` instead of `referenceVar` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.fullShort %>% select(vad_comp, MAIA:NFC) %>% plotCorToOne(., "vad_comp")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(edgeTot, MAIA:NFC) %>% corToOne(., "edgeTot")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'fullShort %>% select(edgeTot, MAIA:NFC) %>% plotCorToOne(., "edgeTot")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(numID, MAIA:NFC) %>% corToOne(., "numID")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'fullShort %>% select(numID, MAIA:NFC) %>% plotCorToOne(., "numID")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(dense, MAIA:NFC) %>% corToOne(., "dense")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'fullShort %>% select(dense, MAIA:NFC) %>% plotCorToOne(., "dense")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(aveDist, MAIA:NFC) %>% corToOne(., "aveDist")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'fullShort %>% select(aveDist, MAIA:NFC) %>% plotCorToOne(., "aveDist")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% corToOne(., "Val_1_Homoph")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_1_Homoph")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% corToOne(., "Val_2_Homoph")
fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_2_Homoph")fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% corToOne(., "Fund_Homoph")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% plotCorToOne(., "Fund_Homoph")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% corToOne(., "Rep_Homoph")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% plotCorToOne(., "Rep_Homoph")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% corToOne(., "Chan_Homoph")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% plotCorToOne(., "Chan_Homoph")[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
---
title: "R Notebook"
output: html_notebook
---



```{r}
library(groundhog)
pkgs <-  c("tidyverse","here", "lmerTest", "sjPlot","broom.mixed", "kableExtra", "ggeffects", "gt", "brms", "bayestestR","ggdist", "pheatmap", "heatmaply","pheatmap","gplots","RColorBrewer")
groundhog.day <- '2022-07-25'
groundhog.library(pkgs, groundhog.day)
here::i_am("Analysis/idmPrelimAnal.Rmd")
```

```{r}
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/named.effects.ref.R")
```


```{r}
fullLong <- arrow::read_parquet(here("Data", "longEpMNet.parquet"))
fullShort <- arrow::read_parquet(here("Data","shortEpMNet.parquet"))
fullLong$subID <- as.numeric(fullLong$subID)
fullData <- fullLong %>% full_join(fullShort, by = c("subID"))
```

# Wordcloud

```{r}
#Create a vector containing only the text
text <- as.vector(fullData$memory)
# Create a corpus  
docs <- Corpus(VectorSource(text))
docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))
docs <- tm_map(docs, removeWords, c("the","and"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df <- data.frame(word = names(words),freq=words)

wordcloud(words = df$word, freq = df$freq, min.freq = 1,           max.words=200, random.order=FALSE, rot.per=0.35,            colors=brewer.pal(8, "Dark2"))
```


# H1: People will evaluate more positively, less negatively (i.e., more favorably) on memories with more downstram dependents.

```{r}
m<-glmer(outdegree ~  Val_1 * Val_2 + ( Val_1 + Val_2 | subID), data=fullData,family="poisson")
summary(m)

m<-lmer(Val_1 ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Val_2 ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)
```

# H2: People will be more certain in memories with more downstream dependents.

```{r}
m<-lmer(Cert ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Cert ~  strength + ( strength | subID), data=fullData)
summary(m)
```

# H3: Memories with more dependents will be more clearly defined and accessible.

```{r}
m<-lmer(Clear ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Clear ~  strength + ( strength | subID), data=fullData)
summary(m)
```
# H5: Memories with more dependents will be more fundamental to how people see themselves, and if they were changed, would change the person.

```{r}
m<-lmer(Fund ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Fund ~  strength + ( strength | subID), data=fullData)
summary(m)
```

# H6: Memories with more dependents will be more important to the person.

## To Self

```{r}
m<-lmer(IM ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(IM ~  strength + ( strength | subID), data=fullData)
summary(m)
```

## To Others

```{r}
m<-lmer(IO ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(IO ~  outdegree * indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("Val_1","Val_2")) %>% plot()

m<-lmer(IO ~  strength + ( strength | subID), data=fullData)
summary(m)
```

# H9: People’s self-report of retrospected emotions during an experience will be associated with how positively or negatively they perceive the experience.

```{r}
m<-lmer(Often ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)
```

# H11: People think more often about memories with more memories causing them.

# The more memories that depend on a given memory, the more people believe "This memory changed me"

```{r}
m<-lmer(Chan ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Chan ~  strength + ( strength | subID), data=fullData)
summary(m)
```

# The more memories that depend on a given memory, the more certain that people feel this experience is representative of who they are.

```{r}
m<-lmer(Rep ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Rep ~  strength + ( strength | subID), data=fullData)
summary(m)
```

# Exploratory Analyses

## Sentiment of memory will be associated with dependencies

```{r}
m<-lmer(vad_comp.x ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(vad_comp.x ~  strength + ( strength | subID), data=fullData)
summary(m)

m<-lmer(vad_pos.x ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(vad_pos.x ~  strength + ( strength | subID), data=fullData)
summary(m)

m<-lmer(vad_neg.x ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(vad_neg.x ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer(vad_pos.x ~  indegree*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer(vad_comp.x ~  strength + ( strength | subID), data=fullData)
summary(m)

m<-lmer(vad_pos.x ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(vad_pos.x ~  strength + ( strength | subID), data=fullData)
summary(m)

m<-lmer(vad_neg.x ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(vad_neg.x ~  strength + ( strength | subID), data=fullData)
summary(m)
```




```{r}
m<-lmer(IM ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(IM ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer(IO ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(IO ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-glmer(outdegree ~  Val_1*Val_2 + ( Val_1+Val_2 | subID), data=fullData, family="poisson")
summary(m)
ggpredict(m, terms = c("Val_1","Val_2")) %>% plot()
```


```{r}
m<-lmer(Val_1 ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Val_1 ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer(Val_2 ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Val_2 ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer(Clear ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Clear ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer(Breadth ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Breadth ~  strength + ( strength | subID), data=fullData)
summary(m)
```




```{r}
m<-lmer(Dist ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Dist ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-glmer(outdegree ~  scale(SO_1) * scale(SO_2) + ( 1 | subID), data=fullData,family="poisson")
summary(m)
ggpredict(m, terms = c("SO_1","SO_2")) %>% plot()
```

```{r}
m<-lmer( Fund ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```


```{r}
fullData$PminN <- (fullData$Val_1-fullData$Val_2)
m<-lmer( PminN ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Fund ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Chan ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Cert ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( IM ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Clear ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Rep ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```


## Indegree Cross-Level Interaction

```{r}
fullData$PminN <- (fullData$Val_1-fullData$Val_2)
m<-lmer( PminN ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Fund ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Chan ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("CESD","indegree")) %>% plot()

m<-lmer( Chan ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("SOS","indegree")) %>% plot()

m<-lmer( Chan ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("SCC","indegree")) %>% plot()
```

```{r}
m<-lmer( Cert ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( IM ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Clear ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Rep ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
```



# Do people higher in self-esteem have more self than other focused memories?

```{r}
fullData$SminO <- fullData$SO_1 - fullData$SO_2

m<-lmer( SminO ~ SE + ( 1 | subID), data=fullData)
summary(m)
```


```{r}
fullShort <- do.call(data.frame,                      # Replace Inf in data by NA
                   lapply(fullShort,
                          function(x) replace(x, is.infinite(x), NA)))
corMat <- fullShort %>% select(edgeTot:NFC) %>% cor(fullShort,use="pairwise.complete.obs")

outphm <- pheatmap(corMat, fontsize_row = 6, fontsize_col = 6, angle_col = 45, angle_row =45, width=100, height = 200 )

heatmaply_cor(round(corMat,3), Rowv=outphm[[1]], Colv=outphm[[2]], revC=TRUE, fontsize_row = 2.5, fontsize_col = 2.5, angle_col = 45, angle_row =45,  limits = c(-1, 1), colors = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100) )
```

```{r}
fullShort %>% select(vad_comp, MAIA:NFC) %>% corToOne(., "vad_comp")
fullShort %>% select(vad_comp, MAIA:NFC) %>% plotCorToOne(., "vad_comp")
```


```{r}
fullShort %>% select(edgeTot, MAIA:NFC) %>% corToOne(., "edgeTot")
fullShort %>% select(edgeTot, MAIA:NFC) %>% plotCorToOne(., "edgeTot")
```

```{r}
fullShort %>% select(numID, MAIA:NFC) %>% corToOne(., "numID")
fullShort %>% select(numID, MAIA:NFC) %>% plotCorToOne(., "numID")
```

```{r}
fullShort %>% select(dense, MAIA:NFC) %>% corToOne(., "dense")
fullShort %>% select(dense, MAIA:NFC) %>% plotCorToOne(., "dense")
```

```{r}
fullShort %>% select(aveDist, MAIA:NFC) %>% corToOne(., "aveDist")
fullShort %>% select(aveDist, MAIA:NFC) %>% plotCorToOne(., "aveDist")
```

```{r}
fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% corToOne(., "Val_1_Homoph")
fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_1_Homoph")
```

```{r}
fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% corToOne(., "Val_2_Homoph")
fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_2_Homoph")
```

```{r}
fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% corToOne(., "Fund_Homoph")
fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% plotCorToOne(., "Fund_Homoph")
```

```{r}
fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% corToOne(., "Rep_Homoph")
fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% plotCorToOne(., "Rep_Homoph")
```

```{r}
fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% corToOne(., "Chan_Homoph")
fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% plotCorToOne(., "Chan_Homoph")
```










